High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. We present learning-based formulations for solving the problem in the bird's eye view and ego-view. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train our model. Perhaps surprisingly, we show that such models can generalize to real world distributions. The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.8 million images. We make the data available to the public at https://www.argoverse.org/av2.html#mapchange-link, along with code and models at https://github.com/johnwlambert/tbv under the the CC BY-NC-SA 4.0 license.

翻译：高清晰度(HD)地图变化探测是确定感官数据和地图数据何时因真实世界的变化而不再相互一致的任务。我们收集了这项任务的第一个数据集,我们赋予了信托机构以权力,但通过挖掘9个月多的自主车队操作的数千小时数据来核查(TbV)数据集。我们提供了在鸟眼和自我视野中解决问题的基于学习的配方。由于真实的地图变化并不频繁,而且矢量地图易于合成操作,我们靠模拟数据来培训我们的模型。也许令人惊讶的是,我们展示了这些模型能够概括到真实世界分布。根据CC BY-NC-SA 4.0 许可证,由在六个北美城市收集的地图和日志组成的数据集是迄今为止最大的AV数据集之一,有780多万张图像。我们向公众提供这些数据,同时提供代码和模型见 https://github.com/johnwlambert/tbv。